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Routing action-relevant VLA tokens across layers unlocks dramatic sample efficiency gains for robotic manipulation RL.
Robot-specific fine-tuning may not be necessary for complex manipulation: structured in-context memory and execution-receipt grammar allow frozen frontier VLMs to match specialized hybrid VLA policies within two points.